lstm_weather.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import dateutil.parser\n",
    "import datetime\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.contrib import learn\n",
    "\n",
    "from pymongo import MongoClient\n",
    "from bson.objectid import ObjectId\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from lstm import lstm_model\n",
    "from data_processing import generate_data, load_csvdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LOG_DIR = './ops_logs/lstm_weather'\n",
    "TIMESTEPS = 10\n",
    "RNN_LAYERS = [{'num_units': 5}]\n",
    "DENSE_LAYERS = [10, 10]\n",
    "TRAINING_STEPS = 100000\n",
    "BATCH_SIZE = 100\n",
    "PRINT_STEPS = TRAINING_STEPS / 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transform QCLCD Data Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# downloaded weather data from http://www.ncdc.noaa.gov/qclcd/QCLCD\n",
    "def load_weather_frame(filename):\n",
    "    #load the weather data and make a date\n",
    "    data_raw = pd.read_csv(filename, dtype={'Time': str, 'Date': str})\n",
    "    data_raw['WetBulbCelsius'] = data_raw['WetBulbCelsius'].astype(float)\n",
    "    times = []\n",
    "    for index, row in data_raw.iterrows():\n",
    "        _t = datetime.time(int(row['Time'][:2]), int(row['Time'][:-2]), 0) #2153\n",
    "        _d = datetime.datetime.strptime( row['Date'], \"%Y%m%d\" ) #20150905\n",
    "        times.append(datetime.datetime.combine(_d, _t))\n",
    "\n",
    "    data_raw['_time'] = pd.Series(times, index=data_raw.index)\n",
    "    df =  pd.DataFrame(data_raw, columns=['_time','WetBulbCelsius'])\n",
    "    return df.set_index('_time')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load The Data as CSV \n",
    "This is QCLCD data for PDX. It is what will be used to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# scale values to reasonable values and convert to float\n",
    "data_weather = load_weather_frame(\"data/QCLCD_PDX_20150901.csv\")\n",
    "X, y = load_csvdata(data_weather, TIMESTEPS, seperate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run The Model and Fit Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_model_dir': None, '_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_tf_random_seed': None, '_task_type': None, '_environment': 'local', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f8d909fb910>, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_num_worker_replicas': 0, '_task_id': 0, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_evaluation_master': '', '_keep_checkpoint_every_n_hours': 10000, '_master': ''}\n"
     ]
    }
   ],
   "source": [
    "regressor = learn.SKCompat(learn.Estimator(\n",
    "    model_fn=lstm_model(\n",
    "        TIMESTEPS,\n",
    "        RNN_LAYERS,\n",
    "        DENSE_LAYERS\n",
    "    ),\n",
    "    model_dir=LOG_DIR\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:267: __init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.\n",
      "Instructions for updating:\n",
      "Monitors are deprecated. Please use tf.train.SessionRunHook.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.mean_squared_error instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:539: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.compute_weighted_loss instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.add_loss instead.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Restoring parameters from ./ops_logs/lstm_weather/model.ckpt-100000\n",
      "INFO:tensorflow:Saving checkpoints for 100001 into ./ops_logs/lstm_weather/model.ckpt.\n",
      "INFO:tensorflow:loss = 0.198107, step = 100001\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:662: calling evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:662: calling evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.mean_squared_error instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:539: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.compute_weighted_loss instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.add_loss instead.\n",
      "INFO:tensorflow:Starting evaluation at 2017-06-23-02:58:10\n",
      "INFO:tensorflow:Restoring parameters from ./ops_logs/lstm_weather/model.ckpt-100001\n",
      "INFO:tensorflow:Finished evaluation at 2017-06-23-02:58:10\n",
      "INFO:tensorflow:Saving dict for global step 100001: global_step = 100001, loss = 0.147549\n",
      "WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.\n",
      "INFO:tensorflow:Validation (step 100001): loss = 0.147549, global_step = 100001\n",
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    {
     "name": "stdout",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 499.753\n",
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      "INFO:tensorflow:loss = 0.230032, step = 199801 (0.188 sec)\n",
      "INFO:tensorflow:global_step/sec: 518.731\n",
      "INFO:tensorflow:loss = 0.225829, step = 199901 (0.193 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 200000 into ./ops_logs/lstm_weather/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 0.201507.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.mean_squared_error instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:539: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.compute_weighted_loss instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.add_loss instead.\n",
      "INFO:tensorflow:Restoring parameters from ./ops_logs/lstm_weather/model.ckpt-200000\n"
     ]
    }
   ],
   "source": [
    "# create a lstm instance and validation monitor\n",
    "validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],\n",
    "                                                     every_n_steps=PRINT_STEPS,\n",
    "                                                     early_stopping_rounds=1000)\n",
    "regressor.fit(X['train'], y['train'],\n",
    "              monitors=[validation_monitor],\n",
    "              batch_size=BATCH_SIZE,\n",
    "              steps=TRAINING_STEPS)\n",
    "\n",
    "predicted = regressor.predict(X['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.446543\n"
     ]
    }
   ],
   "source": [
    "#not used in this example but used for seeing deviations\n",
    "rmse = np.sqrt(((predicted - y['test']) ** 2).mean(axis=0))\n",
    "\n",
    "score = mean_squared_error(predicted, y['test'])\n",
    "print (\"MSE: %f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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58cSPTPOZho21DQkJEBwMd91l+nFbN2qNrbUt526c05dpCt0MzJkDHh7w7LMl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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d7a531c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "all_dates = data_weather.index.get_values()\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "predicted_values = predicted.flatten() #already subset\n",
    "predicted_dates = all_dates[len(all_dates)-len(predicted_values):len(all_dates)]\n",
    "predicted_series = pd.Series(predicted_values, index=predicted_dates)\n",
    "plot_predicted, = ax.plot(predicted_series, label='predicted (c)')\n",
    "\n",
    "test_values = y['test'].flatten()\n",
    "test_dates = all_dates[len(all_dates)-len(test_values):len(all_dates)]\n",
    "test_series = pd.Series(test_values, index=test_dates)\n",
    "plot_test, = ax.plot(test_series, label='2015 (c)')\n",
    "\n",
    "xfmt = mdates.DateFormatter('%b %d %H')\n",
    "ax.xaxis.set_major_formatter(xfmt)\n",
    "\n",
    "# ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d %H')\n",
    "plt.title('PDX Weather Predictions for 2016 vs 2015')\n",
    "plt.legend(handles=[plot_predicted, plot_test])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
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